An Aesthetic of Process is prescribed at the end of What Machines Can’t Do (Robert J. Thomas, 1994) for the sake of a new kind of innovation. A process that brings manufacturing engineers and workers into the fold of product ideation/testing/development. Throughout the case studies in the book (aerospace, aluminum, computers, auto), struggles over the type and meaning of work performed by different teams within the same company emerged. Specifically, the book highlights the efforts on the part of the manufacturing departments to redefine their roles from the ‘shop guys’ who fulfill the orders of product design and R&D, into ‘real engineers’ who add valuable insights and creative ideas that take shape on the shop floor.

In Chapter 3, Between Invention and Convention, Thomas frames the prevailing philosophies of design and manufacturing as a question:

“If product developers and software engineers are the test pilots, the elite units, and the pioneers of the computer business, are those in manufacturing the ground mechanics, foot soldiers, and settlers?” (What Machines Can’t Do, p. 88)

With this mindset as an operating principle for most companies that measure success by units sold, manufacturing departments are tasked with two objectives: ensure the process runs smoothly (i.e. output consistent, production issues arising from design mis-steps resolved) and determine means by which to “lose heads”. These orders from corporate come down the chain and are socially enforced by the product design and R&D departments in virtually all of the cases Thomas explores in his book. In several of the companies, manufacturing exploited this pressure to lose heads when pitching their proposals for new production systems to corporate bosses. But ultimately, some of these systems created new roles for workers whose work became fully automated, while other companies utilized the freed-up workers for different roles where help was needed.

Considering that corporate bosses are looking to increase profit by either making ‘hit’ products or cutting costs, it seems paradoxical that the manufacturing departments would push for automative systems. So it’s worth explaining a few factors at play here. Thomas mentions, at several points throughout his study, that the friction between manufacturing engineers and product design engineers emerges due to the collective understanding of what constitutes “real engineering”. As one manufacturing engineer in the computer company puts it:

“What gets you recognized around here is the whiz-bang product, not the process improvement. The [company] culture still values individual contribution. And your highest-ranked engineer is doing leading-edge technology […] They [product designers] think of us as the guy with a second-rate education and a greasy rag in his back pocket.” (What Machines Can’t Do p.101)

Thomas points to this “perceived inequality, built into the structure and culture of the organization” as incentive for the manufacturing engineers to innovate, sometimes hidden from the view of executives, so that they could obtain the same intellectual respect (and material opportunity) granted to their design counterparts. In the case of the computer company, where manufacturing engineers were working on makeshift experiments in surface mount technology (SMT) in secret, a movement was grown to build a process as product system. SMT was determined to be the technology with the right amount of “whiz-bang” to get at the hearts and pockets of executives. The project was considered a way of legitimizing the manufacturing department’s stature as an innovative arm of the company, a department with real creative talent.

The case studies and resulting theory of the book resonates with the rhetorical climate of today’s economic talk. While unemployment numbers are back to 2007 levels, manufacturing jobs are still leaving the United States. Trump has been on a bit of a tour recently, speaking personally to industrial executives in attempts to keep manufacturing jobs in the States. Concurrently, the Maker Movement is developing into a strong ecosystem of resource centers, products, curricula, and businesses. I think the Maker Movement has a greater shot at accomplishing the aims Thomas set in his book — a culture where Manufacturing is respected as craftsmanship and creativity, and where the glass ceiling of income and respect does not exist beneath those ceilings of Design or R&D.

Draftsmanship precedes craftsmanship, and between the hands and the head is the heart. To forsake sweat, to scoff at the act of production, and to ignore the creative capacity of those whose hands turn the gears of industry, is to forsake the enterprise of the human spirit. Severance of the head from the body will stop us in our tracks. When we don’t respect those who do the making, and we only respect those who do the thinking, our goods will be made without care and our ideas will be thought without experience. This is what’s fundamentally at stake.

By shifting to an economy of process, I think we’ll find a rhythm where we make for need instead of excess. I think we’ll find that our economy becomes one for people, not in spite of people. By focusing and worshipping the artifact, we run the risk of the sin of idolatry. And the sin of idolatry isn’t about paying respect where it’s due, the sin of idolatry is the sin of missing the point. Whereas the artifact stands alone — for your interpretation and personal use — the process of creation, in which the artifact is actually made, requires the presence of the creator. By being conscious of the presence of creators, we are inclined to pay respect to the humanity and energy that is poured into each and every thing that constitutes the systems of sustenance around us. To me, this is what is at stake. We have an opportunity born from the lessons of reflection like Thomas’ and born from movements that encourage us to cultivate curiosity. This opportunity can manifest itself as a distributed network of production, sustenance, craft, and meaning. Your identity will not have to fit into the few shapes allowed by current corporate structures which fundamentally seek to “lose heads” and increase value for shareholders. Your identity will, by necessity, require an adaptation and conjugal resonance with the needs of your body and your community. Make what moves you. Make what you need. But focus on the process and give it meaning. By giving all of our power to artifacts/products, we end up missing the point, which is to say that — there is no ultimate point. No product will provide ultimate service/meaning/enjoyment/sustenance. We will grow bored of every single product, we will come to need nutrition from other types of artifacts. What we need is an ever evolving process that encourages meditation and brings forth innovation. From these processes, and this meta-process of refining the way we think of and make things, we will develop artifacts. Stuff we need, stuff that we enjoy, stuff that makes us reflect, and stuff that represents where we are at with the problems that vex or threaten us.

Thomas sees political struggle when he researches the means by which technology evolves and is developed within an organization. He sees one type of worker struggle for respect (and equal compensation) against the hegemony that benefits other types of workers. The organizations under scrutiny in this book are all hierarchical and stacked with corporate executives at the top. And it’s because of this structure that the struggle between the creative departments emerges. “Who gets what and how much”. Whether it’s financial compensation or creative freedom, it is scarce and its distribution is determined by arbiters of the purse. Within creative organizations, struggle can take creative forms:

“The meaning of a new technology may […] be derived from the social context of its use; however the purpose of a new technology can be understood in terms of the objectives or ends that its proponents (or creators) seek to achieve by means of its use.” What Machines Can’t Do p.227

To me this struggle can make as much, if not a greater, impact outside the borders of a brand. By giving meaning to a technology, to the way it is used and what that use means, makers outside of corporations employ the power of symbol to invoke dignity in the act of creation. We’re at this point where we can create production networks and supply chains that are distributed and nodal. With makerspaces, tool libraries, coworking, and fabrication labs, the platform for open production has [nearly] matured. Now the refinement of the creative process comes next. And it’s this creative process that Thomas points to when he asks “What Can’t Machines Do?”

I sat down on a Friday evening in 2019 months before the pandemic was more realized as global — at that point just a blip in the news among many other things — and I committed to myself to transform some of the latent anxiety I had been feeling about my life’s trajectory into something more structured and intentional.

I was coming off of a strange transition. I put a lot of my effort and thinking into a project about community building that I still put some effort and thinking into. But by 2019 I had gone through a major burnout and transitioned through relationship , employment, and residential changes and community building, while still an aspiration, felt quite far from being a reality. And so I was back to putting time and effort into thinking about the future I wanted to live in — my personal microcosm of meaning and making and meaning-making.

I set a bunch of goals and I feel shy about sharing them but here they are:

Short Term (1–3 years…2020–2023):

Personal Goals

• Produce 3 Art Projects ✅
• Start an Adæpt Webshop & Service ✅
• Travel to Surf & Write — 2 Countries

Professional Goals

• Become a Product Manager ✅
• Product Manager of AI/Data Products
• Become a Certified Cloud Architect
• Start Masters at USC ✅

Mid Term (4–12 years…2024–2032):

Personal Goals

• Write a book
• Creative Talent Agent & Partner
• Building equity on Land

Professional Goals

• Mg. Dir. of Data Eng. & AI Products
• 3 streams of income — Product, Mgmt, Legal
• Legal Work — IP/M&A/Contracts/Regulation

Long Term (13–21 years…2033–2057):

Personal Goals

• Community Steward, Elder, Advisor
• Helping to raise children
• Traveling & Creating

Professional Goals

• Productive Firm Steward, Elder, Advisor
• Teaching, Researching, Guiding Lab-Studio
• Partner in Managing Project Financing

Retirement (~2058+)

Personal Goals

• Community Steward, Elder, Advisor
• Helping to raise children
• Traveling & Creating

Professional Goals

• Board member of Product Co-Op
• Speaker & Writer
• Strategic Partner for AI Products

This list is different from its first incarnation in 2019; at that point I hadn’t thought of applying/attending USC for the M.Sc. in IDBT. I was imagining that the next step in education I would take would be in legal — I think the idea there has been that a law degree and the license that follows it enables a set of abilities that are rare and sought after and that would be interesting — but the thing that stands out and kept coming up has been the desire to work as a product manager and then the desire to ultimately work on AI Products.

In 2021 I hit the goal of becoming a product manager and for that I’m grateful. And while I wish that I had done more between 2019 and now to deepen my abilities and understanding of all the things that constitute AI, I’m here in 05/05/2023 with a resolve to follow through on this goal.

I took a machine learning course in my grad program and using models, playing with the data to get the models to respond in ways I wanted them to, and reporting back on what those responses were like was all a rewarding exercise. I’m feeling excited about digging deep into “not knowing” again. And so, recently I’ve signed up for a couple courses to push me along in this goal:

1. Dan Shipper (from Every) is teaching a course on building chatbots with the Open AI API

2. Jon Reifschneider’s course on AI Product Management via Coursera

As I take these courses, I’ll complement this knowledge with other things I’m learning and hope to coalesce them into principles, practices, projects, and thoughts. This will be the first in a series of posts I do about my journey becoming an AI Product Manager.

So — reporting back from Week 1 of this Path:

Session 1 of Dan’s course was great; I’m excited to be a part of the Every community and alongside the learners in this cohort. We went over some high ground on what to expect and how to operate. We’ll be leaning on ChatGPT as much as possible for learning to do the coding we need to do (in the same way + alongside using traditional references such as Google and StackOverflow)

We’re also using Replit, which I’m just getting into and love and recommend. Here’s the code I made with ChatGPT’s help (it mostly wrote the code):

import openai
import os

# Set the API key
openai.api_key = os.getenv('OPENAI_API_KEY')

def chat_with_gpt(prompt, conversation_history):
    # Add the user's message to the conversation history
    conversation_history.append({"role": "user", "content": prompt})

    # Generate a response using the conversation history
    response = openai.ChatCompletion.create(
        model='gpt-3.5-turbo',
        messages=conversation_history,
        max_tokens=150,
        n=1,
        stop=None,
        temperature=0.8,
    )

    # Extract the response text and add it to the conversation history
    response_text = response.choices[0].message['content'].strip()
    conversation_history.append({"role": "assistant", "content": response_text})

    return response_text

def main():
    print("Welcome to the GPT-3.5 chatbot! You can start chatting now. If you want to exit, type 'quit'.")
    
    # Initialize an empty conversation history
    conversation_history = [{"role": "system", "content": "You are chatting with a GPT-3.5 powered AI."}]
    
    while True:
        user_input = input("You: ")
        
        if user_input.lower() == "quit":
            print("Goodbye!")
            break
        
        response = chat_with_gpt(user_input, conversation_history)
        print("GPT-3.5: " + response)

if __name__ == "__main__":
    main()

Throw this into your Replit, put your OpenAI key into your Replit Secrets (get a CC linked to your OpenAI account) and bam you can start messing with the API yourself 🤙

Week 1 of Jon’s AI Product Management Class has been cool as well. Going over the history of AI, I appreciate how far back this endeavor of building “cognitive machines” goes

And it’s also given some great context on the relationships between the different fields that make up AI:

My aim is to understand “the how” as well as I can, so that as I start becoming better at defining “the why” as a product manager. Moreover, I want to understand the UX/UI principles that are applicable to building products that surface or leverage AI — and how these principles may be different depending on the extent to which AI is used and how central it is to the product interface.

There’s a space for feeling that lies
somewhere in between
infatuation
endearment
gratitude
wonder

It can be tied to a person, this feeling.
For me, right now, it most definitely is.
And it is by virtue of being tied to another soul
that this feeling also plays with the edges
of emotion in another dimension
fear
uncertainty
vulnerability
tenderness

Without complete knowledge of how this person
might feel in response to the way my heart
feels, there is only an image of some set
of potential futures projected against
the backdrop of my mind.
But this uncertainty is a sign of
something true, precious, significant

Uncertainty is the flat description
of the manifold possibility that is the
future.
Anxiety is our intuition that, in spite
of our best efforts to divine the
future through evidence, omens, and
everything in between,
the only way to the realization of
potential is through the act of taking
risks in the present.

Taking risks means living through
both our bodies and minds, in integration.
This is as opposed to the worried
analysis that inhibits our path to action.
To be caught in the web of your fear
is to lose sight on the infinite
expression occurring in the present.
This is where the tension between the feelings of falling in
love and vulnerable uncertainty resolve.

To have faith
means to practice calm
even when it is sensible to feel terror.
To have faith
means to find comfort in patience
when awaiting the arduous path to a bloom.
To have faith means to apply your spirit and effort
to the continued appreciation and cultivation
of love in all of its
mysterious forms.

There was a time, as a boy, that I
struggled with my religion. With its
prophecies, its doctrines, its structure.
But I did not want to abandon my faith
in some deeper meaning and some
fuller hope. I felt torn, angry, sad about
this struggle in my heart.

During this period of time,
as I was losing my faith,
as my religion was losing hold on me,
my father took me up into the
mountains
up into the
forest
where the trees stood so tall
their tops evade your sight
where the trees live so long,
your family history disappears
before you could reach the center
of their arboreal rings.

And on this trip, we took a walk along
a path that reached through the forest
and up the side of one of these
mountains in the range.

The path was long
and it was steep
and I grew tired
bored
agitated.

My protests were met with a conviction
in my father’s voice that was strong
enough to make me fall silent.

And so we continued our
trek up this mountain
through this forest
with tension between us
but a resolve to continue.

The hike was really not that long.

And what had felt like monotony
as we ventured into the woods
ever deeper
came to be the means through
which our minds were cleared,
as if in preparation for what
came next.

What came next was a meadow.
The first I’d ever seen.

It was so glorious, awe-inspiring, gentle
that it reached into me, past the
bounds of my corporeality and touched
my soul.

The prints left behind by that experience
of the meadow exist within me still.

The edges of the grass
were spotted with snow that mid-July.
Throughout the natural garden
were strewn capillaries
of water from the mountain,
its top so close
it was as if you could break off
the peak to keep in your pocket.

My whole life I had only ever seen
landscapes this beautiful in the
pictures provided by the literature
of my religion.
Their promise of an eternal paradise
a projection of some heavenly
unattainable
truth.

But here, in this meadow,
everything was reconciled.

Supreme Beauty
Peace within Presence

The walk down from the
meadow was pregnant in its silence.
Among the family, we said little to nothing
of what we had just witnessed.

But in my entire body was a vigor
of new life.
Life lived not just in embodiment,
but as a deeply rooted gratitude
of my soul.
I felt reassured that I was both
complete
while also being a part of
some larger whole.

That largeness was beyond my
comprehension, but instead of
causing me trouble
it instilled within me faith.
Faith made of calm, patience, tenacity,
and appreciation.

While nearly twenty years
have passed,
the meadow and its impression
have stayed with me.

I won’t lie — I have not always remembered
the lesson I received that day,
a lesson that beauty is in the present,
its gifts shared in its experience
its power and grace immune to captivity.

But when I see you
when I experience your presence
your work
your beauty
your message
I am consumed by my memory of that
meadow.

You engender the resolution
of an infatuated heart
and its fear of the unknown
I am so very glad to have met you.

Adæpt is now a creative collective and co-op in Long Beach, California!
We will be selling art supplies (e.g. canvas, paint, brushes, notebooks, pens) and hosting/teaching workshops in all kinds of topics. We’ve also set up a digital platform using the open source project Mahara that will serve as the ePortfolio hub for the community that sprouts around Adæpt and any other sister businesses in SoCal that are interested in cultivating themselves and their businesses.

Check out our website and connect with us if you’re interested in collaborating!

Over the past four months I’ve been accelerating down a path that began almost three years ago. I’ll start the story with where I am now — at the end of my UX Design Course with Springboard.

This Medium article is a narrative of the Portfolio I have created throughout my time with Springboard as an aspiring UX Designer; a Capstone atop a hill of wonderfully rough rocks.

My project with Springboard is called Adæpt, though moving forward I’ll type it as Adept and in person I’d say it aloud as Adept. Adept is a Learning Management System (LMS) with a little bit of an Innovation Management System (IMS) thrown in for good measure. These three-letter-acronyms are genres of corporate software by which knowledge is passed on (LMS) or created (IMS). It took me a while to discover these acronyms, but I’m glad that I finally did, because one of the things I’ve learned through the Springboard design program is that “where possible, learn from and utilize the great design work that has been done before you.” For example, in the case of LMS’s, Springboard is a wonderful piece of software in and of itself. The visual patterns it uses are industry standard, which can be seen from competitor platforms like Udacity, Udemy, or Designlab. So when designing Adept, I analyzed decisions made by the Springboard and Udacity product teams to inform my own design decisions. But before I get ahead of myself, let me start from the top.

Research

The following is the Problem Space I decided to design for:

Economic Development and Equity in the City of Long Beach, California

I was born and raised in Long Beach, though I left in 2008 for Los Angeles. LA was my home for seven years before I returned to my city of birth. When I came back, I understood that the rapid change I had seen in LA between 2008 and 2015 was well on its way to our cozy port town. Specifically, gentrification was making itself apparent in ways both big and small. The sweeping construction across the city, adorned with ultra-modern design. The fire hydrant covered with a custom knitted sweater. Upon coming back home, I reconnected with friends who told me their stories of a lack of employment, chronic underemployment, and rising rent. At the same time, I became involved in the local tech scene, through MeetUp events at the co-working space Work Evolution (W.E.) Labs. These two aspects of Long Beach connected directly in my mind. Tech, at large, appears to be the driving economic force behind gentrification. The high salaries of young engineers, who desire downtown urban neighborhoods for living and playing, drive up the costs of rent and general living expenses. This is where I’d like to open up the Solution Space I’ve decided to design within:

The Solution Space is often approached with a set of “How Might We” questions. And here I really started to ask myself (and my invisible team) “How might we help people inspire themselves?”, “How might we help people discover their interests?”, “How might we encourage people to try something new?”, “How might we show people that their talents can align with the kind of labor the market needs?” From these questions I decided to make a little model to work out a solution, which you can take a look at below.

This model became the cornerstone of the research that I began to conduct, through the tools made available to me through Springboard’s platform. The following is how I began.

Themes of Interest:

1. Curiosity Generation

2. Strategic Learning

3. Market/Community Affirmation

The purpose of the research conducted is to identify the way Millennials in Long Beach participate in the workforce through their education (formal or otherwise), career preparation, entrepreneurship, and market analysis. This research will be used in the design and development of an education system made freely available online and tuned to the labor needs of the LA/LB/OC area, with an emphasis on opportunities in the city of Long Beach.

The above is a quote from the first Adept project I executed within the Springboard UX Course, which can be found at this Google Drive link.

The research conducted brought to light a good deal to me, in terms of what potential users are looking for from an e-learning system and how their past experiences as students, workers, and job-seekers informed the professional paths they decided to carve out for themselves. This data was gleaned first from a survey of 33 individuals, who were mainly from my Facebook feed (I know, not a great sample size nor a particularly unbiased place to source data, but it’s a start). Here’s one of the reports I was able to produce using these surveys:

Following User Research, it was time to start analyzing the competition. I performed a Heuristic/Competitive Analysis of Udacity for this section of the Springboard curriculum, which can be found at the Google Drive link here. The competitive analysis was a good way to piece apart what sorts of design approaches and strategies are used in commercial LMS products like Udacity or Springboard. The Heuristic Analysis I did was just for Udacity, but I ultimately did a high-level competitive analysis of Springboard and Udemy as well when I started to design Adept’s Site Map and User Flows. But before I get ahead of myself, let’s turn back to the users, this time by way of Personas and Empathy Maps.

Springboard defines a persona as follows:

A persona is a representation of a type of user. Personas answer the question, “Who are we designing for?” They also help to align strategy and goals to specific user groups.

So I arrived at three different user personas, inspired by the people that engaged with the user research I conducted in the first project. Alix, shown above, is someone who is working, but wants to branch out. Alix is looking to become a freelancer by complementing her existing professional skills with something else that might be in demand in the market; she is also open to one day starting a business. Alix’s Empathy Map brings to light how she came to have the qualities that are described in her Persona.

Combined, Personas and Empathy Maps help designers hone in on a particular type of user, instead of some non-existent “average” user. By creating a person inspired by the real people in our lives, we are more readily able to empathize with those we are building products for. This is especially important when designing in a problem space that has an explicit social element, like economics or equity. You can see my full Persona and Empathy Map document here on Google Drive.

Design

The bridge between Research and Design is the User Story. The User Story is a user need described as a product feature. There are more technical definitions of User Stories, once the software engineering has begun, but in the initial design phase a more informal User Story approach is OK, so long as they are revisited in light of new discovery. Here are a few of the user stories I created for Adept on my first pass:

As a User I want to quickly find trusted learning resources for my practice, because there is so much information online it is hard to know where to begin.

As a User I want to know what community events related to my interest are happening near me, because it is important that I meet potential employers/clients/collaborators for my practice.

As a User I want guidance on discovering where my passion and strengths overlap with market needs, because I want to earn well and deliver quality work.

You can see the full User Story document here on Google Drive.

The next section in Springboard’s UX Course is titled Content Strategy. From Springboard:

According to Kristina Halvorson, CEO of Brain Traffic, “content strategy plans for the creation, publication, and governance of useful and usable content.”

From the User Stories I had a direction to move in with my design. But, as always, I needed to get User input. So I conducted a bit more research, by performing a card-sort. A card-sort is defined by usability.gov as

Card sorting is a method used to help design or evaluate the information architecture of a site. In a card sorting session, participants organize topics into categories that make sense to them and they may also help you label these groups.

I decided to use a card-sorting web app that had some built in statistical tools. It’s from optimal workshop and I recommend it if you ever need to perform a card-sort. You can check out my card-sort results at this Google Drive link.

From the card-sort I was able to make a Similarity Matrix, which is what ultimately informed my Site Map:

This Similarity Matrix illustrates the categories that users grouped the cards into. For example, Problems and Puzzles (at the top left) were grouped together 100% of the time, while Idea Pitch and Hackathon (at the bottom right) were only grouped together 40% of the time.

Based on the card-sort results and the competitive analysis I had conducted earlier, I designed the Adept Site Map using Visio:

The flow from Research to Design was a bit intimidating at first. To go from this conceptual model to a solution that was usable and useful seemed like a leap I wasn’t sure I was ready to make. But the card-sort was the perfect intermediary step. This stage ultimately served as a reminder to always keep the user in mind.

From the Site Map, I had to move into designing User Flows. Optimizely defines User Flows this way:

User flow is the path taken by a prototypical user on a website or app to complete a task. The user flow takes them from their entry point through a set of steps towards a successful outcome and final action, such as purchasing a product.

So now, in a way, I was moving backwards, from the tangible to the conceptual. I had the Site Map in front of me, but now I needed to imagine the steps that a user would have to take to address the User Stories I had created. The User Flow designs happened after work one day; I stayed in the office whiteboarding and diagramming until 10 PM, but it was actually an exhilarating experience for me. I was deep in the process of imagining what it would be like to be an actual user, moving through actual web pages, trying to accomplish actual tasks. Peep the user flows that resulted below:

A brief breakdown of the User Flows:

There are 4 streams that happen somewhat concurrently as the User moves through the entire learning journey on Adept.

Learning Resources. These could be things users learn, discover, or invent that help them do the work they love to do. It’s both filled with content provided by Adept and able to be filled with user-generated content.

The Learning Path. This is the delivery of the content they need to learn their subject of interest.

The Portfolio and Project. This is the space that is filled with content that is generated by the user in the Learning Path, but it can also serve as a space for incubating ideas and sketching them out into products or services. The Portfolio is also a staging space for their polished work, which will face the community.

The Community. This is an in-person community in the city of Long Beach, made up of mentors, learners, entrepreneurs, freelancers, workers, bosses, and everyone else in the spaces affected by tech, design, and entrepreneurship. This serves as a testing and networking ground for the user learning on Adept.

You can download the Visio diagram for Adept’s User Flows from this Google Drive link.

So once the Architecture was defined for Adept, I moved into full-on creation mode. First, with painters tape and sticky notes:

And then in Sketch:

The wireframes can be seen individually, both in analog and digital form, at this Google Drive link here.

Once I made these wireframes, which correspond to the Site Map and User Flows I had created earlier, I used InVision to create a prototype that users could click through:

If you’d like to click around the current version of Adept’s prototype, check out this InVision link (you’ll need to create an account, but it’s free).

Making these wireframes was like magic. To see the project I had begun from the research phase come to life in the design phase was incredibly enjoyable. Because of this, it was easy to move into the next phase of the project, Visual Design:

I use a system for designing visual elements called a Style Tile. This is a template that I’ve come to love from a previous UX 101 course I took with Designlab. The image that inspired my palette can be found here on James Roper’s (the artist) website; it’s called Conception of the unfolding implicate and it’s sick.

Evaluation

It was at this point that I crossed the threshold from Design & Adapt to Evaluate & Measure in the design process. This phase requires going back into research-mode (UX Design is mostly research, if you hadn’t noticed yet), but this time I presented a prototype for the users consideration.

Usability Research is a form of testing in which the users are asked to perform a set of tasks (i.e. some set of the User Stories). In my research, I found that the patterns that were most familiar to my users were those that can be seen in typical Learning Management Systems, like a profile page, a community page, and a learning view. But some of the ideas that I had designed from scratch, like the Inquiry Games or the Sandbox, weren’t immediately understood by my testers. So these are things that I know that I’ll have to design more explicitly as I create higher-fidelity prototypes, and eventually develop as a piece of software. The Usability Research document can be found at this link on Google Drive. As with each of the phases in the design process, Usability Testing will be an ongoing and iterative process. My aim is to be more quantitative with this phase as well (eventually reaching into A/B testing).

Conclusion

This isn’t exactly a conclusion. It’s just a touchpoint. A small stop at which I’m letting you know what I’m up to. But where is Adept going? How is it going to fit into the Long Beach community? How will I find the people who might use this?

So, I think Adept is going to be a work-in-progress for a while, because I’m also going to do the software development behind it too. The goal is to make something usable before years-end; please note I said usable, not perfect, by the way. Adept as an idea is huge and I’m fully aware of that. But the hugeness is exciting, especially as a designer. As a developer, the hugeness is intimidating, but if I approach this project with a process and the tenacity that I’ve been using to get to this point, I think I can get something up for you to play with soon enough.

Adept is a project I’m working on as a part of Uncoded LB, a non-profit in Long Beach with a mission “ To cultivate a passionate, sharing, and inventive local tech culture through open, inclusive engagement with our community.” We have a small repository of learning resources that you can find here, but this is just a momentary stand in for Adept.

Ultimately, I hope to make Adept a piece of the Long Beach Economic Development Blueprint, which is an effort by the City and the LB EconDev Commission “ to define priorities that will lead to creation of businesses, well-paying jobs, and increase the standards of living for our diverse residents.” As a disclaimer, I’m on the board of Uncoded and on the LB EconDev commission. But this project is currently just being worked on by me and without any form of compensation. Which is OK, because I feel incredibly passionate and encouraged by the path I’ve taken.

When all is said and done, I hope Adept is interesting and useful. It will be open-source (both design and development materials), so maybe someone will be able to take some of the pieces I’ve made or the lessons I’ve learned and apply it to their own community LMS/IMS. But regardless what the future holds, I’m really happy right now — in the present. Because this has been an amazing experience.

Thanks for reading and please reach out if you’d ever like to talk about Adept or anything else!

A while back I watched The Big Short. Good movie, I recommend it. Apparently quite historically accurate, as far as blockbusters go.

One thing in that movie stuck out. In a way that hurts, as was intended. Michael Burry is a portrayed protagonist in the film, and as one of the investors with foresight in the story (the most foresight, actually), he is given the role of ‘prophet of collapse’. While other investors eventually discover the mortgage crisis brewing, they are tipped off by the work of Burry — he buys credit default swaps from Goldman Sachs by the ton. An employee of Goldman tips off the investor that tips off others and the story moves from there.

I’m highlighting this Prophet of Collapse role because it is used in the very final frame before the credits roll. As the film relays where the characters are after the collapse and what they are up to, they end with Burry. He has liquidated his hedge fund and is focusing on personal investments. In water.

Upon a quick review of Burry you’ll find an auspicious quote from him: “Ironically, I’m in this book, ‘The Big Short,’ but I’m not a big short. I don’t go out looking for good shorts. I’m spending my time looking for good longs. I shorted mortgages because I had to. Every bit of logic I had led me to this trade and I had to do it. And I had to pull back on equities, because I saw what was coming I thought would affect everything.” [source]

So, with this in mind, let’s look at his investments in water as a “long”.

Scarcity increases value. Burry is looking for value. He is estimating that water will be a scarce commodity in the future. So, he’s investing in water. Cause for pause.

If Burry’s investments pan out, trouble is on the horizon. Access to clean water is correlative with all other indicators of a healthy society, which implies the opposite is true as well. In past posts I’ve voiced concern regarding the effects of climate change on human conflict and migration. Some have pointed to drought as one of the triggers of the Syrian civil war, for example. Therefore, it’s imperative that people put energy into devising new ways of capturing and filtering water from the environment. A corollary to this is the need for an open solution to water capture and filtration, so that users can easily maintain and perhaps even upgrade these systems to suit their needs. Let’s look at some solutions currently under development:

1. WaterGen — A company based out of Israel that initially started out selling systems to the Israeli military. It has now opened up sales to other institutions and individuals. A single small unit can run on less energy than a hair dryer on high heat and produce between 3 and 4 gallons of water a day. The system just needs a source of electricity and it can pull water from the air. WaterGen also sells portable purification systems that run on solar panels. A company with products that would serve people well during natural disasters, infrastructure compromise, ecological distress, or significant social conflict.

2. The Slingshot — The Slingshot is another water vapor distillation system, and it’s differentiating claim is that it can run on any combustible fuel (including cow dung) and produce clinical-grade drinking water from almost any any source. These two factors make the product especially relevant for people that don’t have access to an (optimal) energy infrastructure or clean water. The system runs by means of a Stirling model engine, with its mechanism roughly outlined below:

3. WarkaWater — A water capture structure designed by Arturo Vattori and Andrea Vogler. From their site: “Warka Water is a vertical structure designed to collect/harvest potable water from the air giving an alternative water source to rural population that face challenges in accessing drinkable water. WW not only provides a fundamental resource for life — water — but also creates a social place for the community, where people can gather under the shade of its canopy for education and public meetings […] Air always contains a certain amount of water, irrespective of local ambient temperatures and humidity conditions. This makes it possible to produce water from air almost anywhere in the world. Locations with high rates of aerosol and humidity are best to install WW. WW is a vertical structure designed to harvest potable water from the atmosphere (it collects rain, harvests fog and dew). The objective is to give to the community up to 100 L (26.4 gal) of drinking water every day.”

4. The Dew Bank — A biomimetic water bottle that utilizes the nanoscale hydrophobic structure of the wings on the (onymacris unguicularis) Namibian Desert Beetle. These structures can also be utilized in an industrial setting, which is the pursuit of NBD Nano.

There are many projects, even more than the 4 listed here. Some of this technology seems to be easier to open than others, and some systems seem to require a lower level of maintenance than others. But it is imperative for communities large and small to have leverage over the ways they source, clean, and consume water. Perhaps some convergence of these technologies can enable solutions that scale between individuals and cities of 1M+. A crucial factor is the pace and ease at which these systems can be deployed and implemented.

As far as open source water solutions go, a preliminary Google search will show that most individuals are focused on rainwater capture. This is probably the most sensible approach, especially in the short term, but if the climate is unpredictable or people are on the move, then some of these other technologies need to be considered. So, room to improve and innovate in this space.

Up next on the Abundance Pyramid Series: Shelter

The “Abundance Pyramid” is a high level approximation of human survival and flourishing, published in Abundance: The Future is Better Than You Think, a techno-optimist’s book written by Peter Diamandis and Steven Kotler. The pyramid is designed to break down the worlds problems into strata approachable by engineers and social entrepreneurs. Diamandis utilizes graphs to support his position of optimism:

These statistics are encouraging and Diamandis attributes them to advances in technology and the productivity that arises from technological innovation. But after reading What Machines Can’t Do, I’m convinced that technology in and of itself is not enough to uplift and sustain communities of the working class. So this is the first post in a series exploring the intersection of technology and politics of direct action, as they pertain to fundamental human needs.

This blog post will focus on one of the foundations of the Abundance Pyramid: Food.

____________________________________________________________________________________________

Food scarcity has long been the alarmist problem of the future. Malthus pointed to a future of overpopulation and its plights — famine, disease, and territorial conflict. This is a logical conclusion considering the finite space on the surface of the Earth, and an upper limit of sustenance that Earth’s resources are capable of providing. Addressing these issues requires looking at the issues that come up with, and because of, food production. Growing and distributing food, particularly meat, is energy and labor intensive. Moreover, it requires an enormous amount of water. A solution to these inefficiencies that is often thrown around is vertical-indoor farming.

There are companies around the world exploring this new industry, including Japan, Korea, Los Angeles, and Boston. From growing in clean rooms to shipping containers, the technology of vertical farming is proving itself to be not only possible, but optimal. Vertical Farming has been shown to use 10% of the water of traditional field farming, require substantially less labor than outdoor farming, if performed in sterile conditions it requires no pesticides or herbicides, growing area is increased significantly, and crops can be grown throughout the year (source: verticalfarms.com)

Furthermore, Internet of Things (IoT) technology can be fully integrated into the vertical farming system, such that the harvest is fully monitored and automated. And when agriculture is opened up to automation, the growth, harvest, and processing of food could be optimized and accelerated to meet human needs and provide surplus for the foreseeable future. The FarmBot is a glimpse into this agro-automated industrial revolution.

Beyond plant-based food is lab-grown meat, dairy, and eggs — animal products produced without the suffering or captivity of animals. New Harvest is a non-profit that funds projects devoted to researching methods of cellular meat and animal-by product production.

Considering that the world is expected to reach 9.6 billion by 2050, these food production technologies will likely require extensive utilization to prevent or mitigate mass suffering. Furthermore, it is estimated that between 6 and 7 billion will live in cities by 2050. By designing and implementing systems that produce food in urban centers, where most people will live, distribution costs to both people and the environment can be dramatically reduced. And most food will be delivered to an individual fresher than possible now.

Beyond culturing meat and growing plants indoors using robots and clean rooms, there are projects, technology, and ideas that can empower communities to have more control over their local resources and the knowledge required to use them to their full potential. Projects like the Open Source Seed Initiative aim to empower farmers to retain control over the intellectual aspects of their work:

While projects like Open Source Ecology aim to create open design kits of the industrial tools necessary to steward land — from homesteading to raising crops, and beyond:

When going through the list of these projects it’s hard not to think of the Abundance maxim: “The Future is Better than You Think.” But this is only a potential future, and one that hinges on vision, experimentation, and consistent effort to push for a society that puts people at the center of political/economic systems, utilizing technology that can optimize those systems, and prevent the worst of human nature from coming through.

Considering that the rich would rather prepare for a world ruined by their own greed than distribute wealth across society, collective and direct action is necessary in the design of this potential world now.

That direct action can be seen in the likes of Ron Finley’s Guerrilla Gardening or the Michigan Urban Farming Initiative. Food, and the means of producing it, can lead to profound shifts towards independence for disadvantaged communities. Moreover, collective ownership of the distribution of food, like that pursued by the Long Beach Grocery Cooperative, can be used to ensure that the food consumed in a community is traced back to clean and fair practices, while the money spent on that food stays within the locale of the cooperative.

While new technologies may offer opportunities of optimization or a complete re-imagining of global production and service, politics are required to organize and action labor to design and implement that new technology. Open-source is one means; utilizing blockchain to ensure transparency and trust might be another. Whatever the methodology is, it will need to be driven with an understanding of its impact on community and environment alike. This is particularly true for the production of food as our global population surpasses 9 billion. To me the fundamental question is simple:

“How can we ensure that food is a guaranteed right to every human being on the planet?”

I believe it’s possible, but the answer is ‘To Be Determined’. With enough minds from around the world, my faith is that determination will come when we need it.

calm
in all my heart
where the window meets
the phrasing
of ellipses
sutured still
where necks raised
proud engage the grain
of hard fought land
shorn by the stories
framed in conquest
violent sorrow
massive protest
concrete swamp
leaking taxes
all roads lead back
to home

icon shining in the dark
corrected vision
seething
with a hunger
camping lines
form round the cult
entranced by cobalt powered
portals
pouring light-noise
for our klipot
collective
screaming for fulfillment
shattered sephiroth

alignment lurking in
the background
breeding stars
found under covers
of illusion
unlit
by darkness
pushing outward
what a secret
veiled thinly
we can taste it
hold me closer
bombs keep falling
like the sounds
of church bells ringing
can you hear al-muezzin calling
while all falls down
the word whispers
its just begun

The brush of memory across the
skin in the morning
softly coaxes
longing from the will.

Coffee cooking in a pot
bombing senses with
iterative nostalgia.

You’re now a facsimile of adulthood
and you embrace this phase
with enthusiasm.

Now you seriously iron your clothes.
Now you cordially drag
a razor across your face.

Could it be
the World
spins round
an axis
of remission?

Over & Over
we dive into
destinies
baked with premonitions
of children
we are responsible for,
Saying to ourselves
“I won’t make the
same mistakes
they made.”

Sunshine hits our planet
no matter
what horrendous mistakes
we make.

There is nothing significant
about this,
but I figured it’s as good
a reminder
as any,
here,
in this little poem.

There are two hairs growing between the distal interphalangeal and
proximal interphalangeal joints on
my right thumb.

There are no hairs on the chiral
space of my other thumb.

Now I don’t believe in omens
but I do find this fact peculiar.

Maybe this is a measurable indicator
that I am not yet a full grown human being.
By measures like these, I likely
never will be.

The milieu of my culture
and society is as dense as yours, I’ve heard.
And as I pass through this
gel of slang
my hope to
coexist with you
rests on the merits
of that idea,
that we’re not that
different after all.

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — 
CODA
 — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
The spectacle of space has an effect
on us that is the opposite of emergent,
and not immersive either.

The spectacle of space is the
sort of experience that moves from the
outer limits of embodied identity into
the deepest core of sensual existence.

The spectacle of space carries
code that inculcates consciousness
to direct agency in a way such that
the illusion of absolute movement drives
the illusion the agent has control.

Abstract:

The following paper was a 2014 review of the science and engineering that investigates the phenomenon of dreaming. An addendum as of 2017 has been added at the end. An overview of the field and its prospects is described, followed by the benefits such research will be able to deliver. Current and future work is also described. The paper is oriented around the question “Will it ever be possible to record a dream and view the recording on a screen?”

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Dreams have been long revered yet poorly understood throughout human history. Some of the oldest artwork found depicts the journey of a human soul into the ‘dreamworld,’ and dreams remain a popular subject of contemporary art, indicating the persistence of human intrigue in dreaming. In spite of this lasting curiosity, some pivotal questions remain: where do dreams come from and why do we dream at all? While there are still different opinions on the answers to these questions, research in neuroscience and psychology has already revealed that some of the basic assumptions about dreaming from the twentieth century were incorrect. A new kind of science is being built from the rubble of these insights. The experience of dreaming is being connected to specific parts of the brain and new experimental tools have emerged in recent years that might enable researchers to one day see the dreams of their subjects or perhaps even trigger the onset of dreams. Ultimately it will be the work of neural engineers to build the foundation for the design of these devices that record, induce, and manipulate our dreams.

The Difficulties of Dream Research

The main difficulty with dream research, also known as oneirology, is the challenge it presents to the scientific method. The scientific method holds that in order to draw reasonable conclusions from data, an experimenter must be able to control a variable in the experiment. It is hoped that this controlled variable will validate or invalidate the hypothesis proposed prior to the experiment. Currently, controlling a variable of the dreaming process isn’t possible.

In a personal correspondence, prominent dream researcher G. William Domhoff explained “[T]he problem is that you can’t make [dreams] happen, can’t see them while they are happening, and can’t get any after-the-fact info on them, except through [questionable] verbal reports.” To psychologists like Domhoff, this means that there is a lack of control that “psychologists rightly desire to have.” Aside from this methodological block to oneirology, the research in and of itself is expensive; subjects must be paid to spend a night or more in a lab, and researchers must be willing to do the same [1]. Some solutions to this problem might include automating the roles of researchers so only subjects are in the lab or just collecting dream reports and biological data from a subject sleeping at home.

There appear to be no immediate capital benefits from studying dreaming in a clinical context. This lack of foreseeable profit combined with the difficulty maintaining scientific rigor in the research itself makes for an environment in which there is very light funding and very few opportunities for academic recognition. This combination of poor conditions unfortunately scares off qualified researchers from studying this fascinating and deeply mysterious field. While these conditions make it difficult to study the workings of the dreaming mind, there are many legitimate reasons for future scientists and engineers to think deeply about this subject.

The Benefits of Oneirology

The National Academy of Engineering announced a set of problems at the turn of the millennium, which they have described as “Engineering Grand Challenges.” These challenges represent the greatest problems facing humanity and invite engineers to consider solutions of their own. Included among these challenges are “reverse engineering of the brain,” “enhancement of virtual reality,” and “personalization of medicine.” These are all efforts that would benefit from a deeper understanding of the biological basis of dreaming. In fact, oneirology is intrinsic to the endeavor of reverse engineering the brain. To fully understand cognition, consciousness needs to be explained in terms of the way the brain actually functions [2, 3], and these explanations need to be able to answer questions about the way the mind functions while it is both awake and asleep if they are to be considered complete.

The National Academy of Engineers lists virtual reality (VR) as a necessary tool of the twenty-first century, and research into dreaming is the key to the holy grail of VR: full immersion. A strong evolutionary explanation of dreaming doesn’t currently exist, yet it is a fact of life that nature has crafted a fully immersive virtual experience for us each night. With deeper investigation into the neural networks responsible for dreaming coupled with technological advances in brain-computer interfacing, it is not difficult to conceive of a system that uses the dream as the medium for constructing a virtual world.

The NAE imagines virtual reality to be an indispensable tool in the future of instruction and experimentation in fields such as medicine, warfare, and education. In fact, virtual reality is already a central tool in teaching pilots how to fly and surgeons how to operate. Engineered dreaming can be expected to enhance the utility of these tools and enable teachers to construct more realistic exercises for students to train with.

The final challenge mentioned, the personalization of medicine, will be deeply affected by dream research when a medical theory of dreaming emerges. Dreaming is one of the most personal experiences that a person may have. Presently there is no way to know for sure whether or not a dream can broaden a caregiver’s perspective on the wellness of a patient, but this is likely the case. Dreams are a result of the systemic intersection between our minds and bodies. Therefore it seems that by mining our deeply personal and (mostly) uncontrollable psychological events we can better analyze the state of our health. Imagine the value in thoroughly analyzing the content of a patient’s dreams to accurately diagnose and effectively treat mental or physical illness. This clinical approach stretches back from Freud’s psychoanalysis to the Asclepeion of ancient Greece. And while psychology has come a long way since the 1900’s, dreams remain an attractive diagnostic tool for doctors that desire a more holistic perspective on their patients.

Current State of Research

Dreams had been the object of twentieth century academic inquiry under Freud and Jung for many years. Unfortunately, the contention between the two camps and the lack of tools that produced concrete evidence about the dreaming brain led to a dwindling in research as the century marched forward. But in 1953, two graduate students studying the physiology of sleeping subjects discovered that rapid eye movements (REM) were often associated with reports of dreaming [4].

This finding greatly renewed interest in the study of dreaming and its connection to the body. This landmark work led to the discovery that sleep occurs in different stages. These different stages of sleep indicated to scientists that a subject’s propensity to dream increased as the night wore on, which happened in parallel with increasing episodes of REM, as shown by the widening gaps of REM stage sleep in Figure 1. In the 1960’s a French researcher named Michel Jouvet investigated the neurochemistry responsible for these shifting physiological states in subjects during sleep [5]. In his research with cats, he found that aminergic neurotransmitters like dopamine and serotonin were at least partially responsible for the waking portion of consciousness, known as vigilance. In sleeping animals, he found that acetylcholine acted in the body at high concentrations, and was therefore most likely to be tied into the chemistry responsible for sleep. Jouvet’s research seemed to point to dreams as a sort of ‘paradoxical’ chemical state of the body, where amines and acetylcholine fought for control of consciousness, producing a state of vigilance within an otherwise unconscious body. These findings have led some self-experimentalists to utilize drugs to induce dreams, like galantamine, calea zacatechichi and even nicotine, which apparently modulate the amine-acetylcholine balance in the body through their metabolic action [6].

Outside of a pharmacological domain, there is research being done using subjects that have the ability to willfully enter into a lucid dream. Lucid dreaming is a state of consciousness in which a person is in a dream and concurrently aware that they are in a dream. Using a technique developed in the 1980’s by Stanford researcher Stephen LaBerge, lucid dreaming subjects signal to researchers that they are conscious while in the dream state by moving their eyes in a synchronized pattern [7]. This method of synchronized eye movement convinced most researchers of the reality of lucid dreaming in the 1980’s, even though Tibetan Buddhist monks have written about the phenomenon for hundreds of years [7]. Recent research using functional Magnetic Resonance Imaging (fMRI) on lucid dreaming subjects has compared the activity of the motor cortex while awake and its activity while subjects are in a lucid dream: the subjects clenched their fists while awake, then imagined clenching their fists (again while awake) and then reported clenching their fists while in a lucid dream [8]. The researchers found significant correlation in brain activity between the three scenarios.

Figure 3 illustrates the conclusions drawn by the researchers in this study: the neural regions involved in actually clenching the fist and the neural regions associated with dreaming about clenching the fist are the same. The main difference in the brain between dreaming and waking states is the total area of neural regions involved and the magnitude with which those neurons are acting. This work in oneirology is revolutionary because dreams are often thought to be unpredictable events with unknowable contents until the subject is awoken and interviewed, and yet here the experimenters could confidently state they knew the actions of their subject while they were dreaming.

This research is unique because of its methods: the use of a subject with the ability to lucid dream at will and the use of tools that allow for high resolution images of the brain. It ultimately seems as though this will be the standard method of inquiry for the neural engineers that wish to understand how dreaming works. Lucid dreaming is a technique that a growing number of people are using to more fully engage with their dreams. As the regions of the brain responsible for lucid dreaming is mapped, and those brain regions are mathematically modeled, engineers will be able to develop technology that facilitates its occurrence.

A new field of product design and engineering is emerging as a result of overlap in public interest between lucid dreaming and wearable technology. While this market is relatively new, products related to recording dream content and documenting sleep patterns have been supported by tens of thousands through the crowd-sourcing site Kickstarter, indicating a demand in the market for more information about the way we sleep and dream. Among these products are apps like alarm clocks that help someone to remember and record their dreams and sleeping masks that flash lights onto the eyes to help achieve a lucid dream.

Collecting data on dreaming across populations and across the life of a person is an obvious and necessary first step in understanding the environmental conditions from which dreams emerge, as this information will be key in the design of engineered dreaming tools. But once these products start to bring data back to the scientists and engineers who designed them, a standard practice of dream data analysis must be constructed to the uncover the patterns that emerge.

Because engineering has its foundations in science, it must analyze data as objectively as possible. This requirement seems obvious, but it is a point of contention in the field of dream research because of the heavy influence of psychoanalysis on dream interpretation. According to modern research in cognitive psychology, dreams do not seem to be intensely symbolic or thematic, nor do they seem to be the by-product of random brain stem activity at night, as some scientists have argued [9]. Instead, evidence from the content analysis of thousands of dream reports has shown that most dreams are rather mundane reproductions of the daily life of a dreamer. All of this prior research is being considered as the current wave of dream technology products come to market. These devices will likely stream data collected from the users to a centralized source that will enable dream researches to study patterns of dreaming, much like the way social media data is analyzed for advertisement purposes. With all of this data from so many users, engineers and scientists will be equipped to drive further research and development in the creation of consumer dream technology.

The largest existing online dream report database is the website dreambank.net which was constructed by researchers at the University of California Santa Cruz. This database holds a library with over 20,000 dream reports from volunteers between the ages of 7 to 74 with some of the reports spanning several decades across the life of a subject. This is a platform anyone can browse but it is mostly used within a small academic circle of researchers. Hopefully, the data collected from the new consumer devices that are beginning to hit the market will allow for the construction of a new database, with reports from people worldwide that choose to volunteer their dreams for research. As more data is aggregated across larger groups of people over longer periods of time, anthropological studies of dreaming, especially as it relates to community and storytelling, will continue to develop, deepening the emerging field of the ‘digital humanities’. This field uses computational tools from the sciences and engineering to gain new perspectives on social phenomena. And although dreams are among our most personal experiences, they transform into stories once shared with other people. By analyzing the way people describe, respond, and reflect on their dreams, researchers will get a macroscopic view of how the process of dreaming blossoms from an isolated event in our brains into a social artifact in and of itself.

The Future of Dream Research

As neuroscientists map the regions of the brain responsible for dreaming and mathematicians construct the tools necessary to model the way these regions work, engineers have an opportunity to interface with the dreaming brain in unprecedented ways. Currently the extent of engineering projects that integrate dreaming are limited to gadgets that flash lights onto your eyelids or remind you to record your dreams when you wake up. Modern medical imaging tools and clearer understanding of the brain are enabling technology to take on a new set of engineering challenges. By using images of a brain actively dreaming and feeding this data into a working model of the brain’s visual and imaginative systems, it is theoretically possible to stream the visual content of a subject’s dream onto a screen.

Brain decoding is the science of interpreting signals within the brain in order to reconstruct the sensory experiences had by a subject [11]. This research is happening for the design and construction of effective brain-computer interfaces for patients who lack limbs or the ability to move. Moreover, understanding the encoding/decoding methods of the brain inspire engineers in the design of new robotics and AI [12]. The additional effects of this research are not difficult to imagine when considering the lab of Dr. Jack Gallant at UC Berkeley. Gallant’s team has been able to reconstruct the visual experience of their research subjects using imaging data from fMRI.

Two things are worth note: the reconstructed “movies” are very fuzzy, but they are astonishingly similar to the video clips the subjects were made to watch. And second, the data used to make this reconstruction represents blood flow through the brain [12]. This blood flow was the input, the fuzzy videos are the output. They accomplished this by building a digital model of what they think is going on in a part of the visual cortex in the brain. In effect, Gallant and his team have made a crude mind-reading device

While this work might evoke concerns about privacy that resonate with current news, it is astonishing to consider the possibilities, particularly as it relates to the field of oneirology. Another lab in the field of brain decoding doing happens at USC, in the lab of Ted Berger. Berger is part of a team that has managed to construct a cortical prosthesis that enables patients who have suffered from strokes that cause amnesia to create new long-term memories [13]. This research is significant in that it does more than just decode signals from the brain; it actually encodes signals back into it. Using microelectrodes that sense and conduct current from brain tissue, the prosthesis is a small chip with an onboard computational model of a portion of the hippocampus. While this model may not exactly represent the structure or function of its biological counterpart, it works well enough to enable former amnesiacs to form new long-term memories.

Fundamental neuroscience research has revisited old data in light of new insights, some coming from this work in decoding. Of interest to those studying dreaming, a new network involved in daydreaming and idle thinking has been uncovered. The regions involved in this network are being probed to determine their function while a subject is feeling, learning, or solving problems. This substrate has been termed the Default Mode Network (DMN) and there is evidence accruing that many parts of this network underlie dreaming itself [14]. While the DMN is mostly being studied in subjects who are awake, recent research has shown that there is extensive overlap between the DMN and the dreaming network. This overlap articulates the map of the brain and its functions that are relevant to dreams and imagination.

The future of psychology and neuroscience is truly indeterminable when you consider the gains to be made from the design of a device that can read the most private experiences of the human mind. Science, medicine, and education are just a few of the fields that will be impacted by the ongoing revolution in neuroscience, particularly as that revolution relates to dreams. And as neuroscience uncovers the means by which the brain gives rise to conscious experience, engineers will have an opportunity to interface and design for the mind in direct ways. Prosthetics that feel like limbs, remote controlled bodies that enable users to feel as though they are present in far off places, and shared dream experiences are all subjects of countless science-fiction stories, and yet when one considers the current successes of neural engineering they don’t seem as far-fetched as they used to. Billions of dollars in funding from the United States and the European Union are making their way into these kinds of projects due to recently announced initiatives to map the structures and functions of the brain. All of these conditions leave the next generation of neural engineers well equipped to tackle the problems that have faced humanity for generations, like the question of what happens when we dream.

2017 Addendum:
Since the writing of this paper a great deal has occurred. One of the greatest shake-ups in the history of oneirology was published in May of 2014, soon after I had written this paper. 

Ursula Voss and others published that they had been able to induce lucid dreams by stimulating the dorsolateral Prefrontal Cortex (dlPFC) with transCranial Alternating Current stimulation (tACS) set at 40 Hz [15]. This signal apparently coaxes the dlPFC into something akin to an awakened awareness while the subject is still asleep. Soon after, Martin Dresler (from the "clenched-fist" research) built off of Voss's work to map and compare the correlates of dreaming to the correlates of psychosis[16]. I believe this is the way dream researchers will begin to pull significant funding into the discipline. 

Following Voss's discovery, a Kickstarter project was started for the consumer-grade Lucid Dreamer device, based on her 40 Hz research. The Kickstarter was funded, but ultimately cancelled, as the researchers felt they were not getting consistent enough results. The project/company still exists however and you can follow them at http://www.luciddreamer.com/.

Flash forward to 2016 - I sit down with Domhoff for the first time, after years of email correspondence. He tells me he has kept pushing on his cognitive approach to dreaming and has continued to connect his work with research in the Default Mode Network (DMN) and feels there is a trove of theory yet to be discovered lying in this neural system. 

Then, in March 2017, a new paper comes out of the lab of renowned consciousness/dream researcher Giulio Tononi, claiming to have not only uncovered the neural correlates of dreaming, but to connect those correlates to activity in the visual processing systems of the brain[17]. In his paper he states that he was able to roughly predict whether the dreaming subjects experienced "seeing a face" or "being inside vs. outside". He builds off of research that has compared the brain states of subjects who are 'perceiving vs. thinking'. Tononi builds off of some of Voss's work, but he doesn't use her technique of tACS to induce lucid dreaming in the subjects of his experiment.

When I shared Tononi's paper with Domhoff, he was surprised there was not mention of the DMN or the Cognitive theory of dreaming. There are different schools of thought within dream research and sometimes things can be a bit political between them (i.e. Freud vs. Hobson vs. Domhoff). Perhaps this was a factor in Tononi's investigation.

It is worth noting that Jack Gallant's lab has been able to reconstruct semantic thought on what a subject is seeing [18], and Ted Berger and his lab are now part of Kernal, a company in direct competition with Elon Musk's neurotechnology effort, Neuralink[19].

Domhoff thinks the immediate future of dream research might be realized with functional Near-Infrared Spectroscopic imaging (fNIRS) which can be done affordably, portably, and with relatively high accuracy. Facebook recently revealed at their 2017 F8 conference that it is looking into this technology for brain-computer interface technology[20][21]. So between fNIRS, maps of the dreaming brain, and the tools used to collect dream reports upon awakening (think Siri/Alexa), we may be moving from the study of dreams (oneirology) to the engineering of dreams (oneirothetics). 

P.S. If you have any dreams you'd like to share, or if you'd like to contribute to dream research, please reach out to the team at http://www.dreambank.net/

Bibliography

[1] Philipp G. Sa¨mann, Renate Wehrle, David Hoehn, Victor I. Spoormaker, Henning Peters, Carolin Tully, Florian Holsboer and Michael Czisch, “Development of the Brain’s Default Mode Network from Wakefulness to Slow Wave Sleep,” Cerebral Cortex, vol. 21, no. 9, pp. 2082–2093, Sept. 2011

[2] Donna Rose Addisa, Ling Panb, Mai-Anh Vub, Noa Laiserb, Daniel L. Schacter, “Constructive episodic simulation of the future and the past: Distinct subsystems of a core brain network mediate imagining and remembering,” Neuropsychologia, vol. 47, pp. 2222–2238, Sept. 2008

[3] Donna Rose Addis, Katie Knapp, Reece P. Roberts, Daniel L. Schacter, “Routes to the past: Neural substrates of direct and generative autobiographical memory retrieval,” NeuroImage, vol. 59, pp. 2908–2922, Oct. 2011

[4] Draulio B. de Araujo, Sidarta Ribeiro, Guillermo A. Cecchi, Fabiana M. Carvalho, Tiago A. Sanchez, Joel P. Pinto, Bruno S. de Martinis, Jose A. Crippa, Jaime E.C. Hallak, Antonio C. Santos, Schacter, “Seeing With the Eyes Shut: Neural Basis of

Enhanced Imagery Following Ayahuasca Ingestion,” Human Brain Mapping, vol. 33, pp. 2550–2560, Sept. 2011

[5] Michel Jouvet, “Biogenic Amines and the States of Sleep,” Science, vol. 163, no. 3862 pp. 32–41, Jan. 3 1969

[6] E Aserinsky, N Kleitman, “Regularly occurring periods of eye motility and concomitant phenomena, during sleep”. Science vol. 118, no. 3062, pp. 273–274, Sept. 1953

[7] J. Allan Hobson, Dreaming: An Introduction to the Science of Sleep. Oxford: Oxford UP, 2002.

[8] David Foulkes, Dreaming: A Cognitive-psychological Analysis. Hillsdale, NJ: L. Erlbaum Associates, 1985.

[9] G. William Domhoff, The Scientific Study of Dreams: Neural Networks, Cognitive Development, and Content Analysis. Washington, DC: American Psychological Association, 2003.

[10] Deirdre Barrett, and Patrick McNamara. The New Science of Dreaming. Westport, CT: Praeger, 2007.

[11] Martin Dresler, Stefan P. Koch, Renate Wehrle, Victor I. Spoormaker, Florian Holsboer, Axel Steiger, Philipp G. Sämann, Hellmuth Obrig, and Michael Czisch. “Dreamed Movement Elicits Activation in the Sensorimotor Cortex.” Current Biology 21.21 (2011): 1833–837. Print.

[11] Shinji Nishimoto, An T. Vu, Thomas Naselaris, Yuval Benjamini, Bin Yu, and Jack L. Gallant. “Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies.” Current Biology 21.19 (2011): 1641–646.

[12] Doree Armstrong, and Michelle Ma. “Researcher Controls Colleague’s Motions in 1st Human Brain-to-brain Interface.” UW Today. N.p., 27 Aug. 2013. Web. 13 Feb. 2014.

[13] T. Horikawa, M. Tamaki, Y. Miyawaki, and Y. Kamitani. “Neural Decoding of Visual Imagery During Sleep.” Science 340.6132 (2013): 639–42. Print.

[14] Theodore W. Berger, Robert E. Hampson, Dong Song, Anushka Goonawardena, Vasilis Z. Marmarelis, and Sam A. Deadwyler. “A Cortical Neural Prosthesis for Restoring and Enhancing Memory.” Journal of Neural Engineering 8.4 (2011): 046017. Print.

[15]Ursula Voss, Romain Holzmann, Allan Hobson, Walter Paulus, Judith Koppehele-Gossel, Ansgar Klimke & Michael A Nitsche. “Induction of self awareness in dreams through frontal low current stimulation of gamma activity.” Nature Neuroscience 17, 810–812 (2014) doi:10.1038/nn.3719. Print.

[16]Martin Dresler, Renate Wehrle, Victor I. Spoormaker, Axel Steiger, Florian Holsboer, Michael Czisch, J. Allan Hobson. “Neural correlates of insight in dreaming and psychosis.” Sleep Medicine Reviews 20 (2015) 92e99. Print.

[17]Francesca Siclari, Benjamin Baird, Lampros Perogamvros, Giulio Bernardi, Joshua J LaRocque, Brady Riedner, Melanie Boly, Bradley R Postle, & Giulio Tononi. “The neural correlates of dreaming”. Nature Neuroscience, 10 April 2017, doi:10.1038/nn.4545. Published online.

[18]Alexander G. Huth, Tyler Lee, Shinji Nishimoto, Natalia Y. Bilenko, An T. Vu, and Jack L. Gallant. “Decoding the Semantic Content of Natural Movies from Human Brain Activity”. Front. Syst. Neurosci., 07 October 2016 | https://doi.org/10.3389/fnsys.2016.00081. Online.

[19]http://www.economist.com/news/science-and-technology/21719774-do-human-beings-need-embrace-brain-implants-stay-relevant-elon-musk-enters

[20]http://www.kurzweilai.net/what-if-you-could-type-directly-from-your-brain-at-100-words-per-minute

[21]https://developers.facebook.com/videos/f8-2017/f8-2017-keynote-day-2/

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

In my life is a narrative that runs parallel to our global story, although it is uniquely my own. Born into a family of Jehovah’s Witnesses, my mind was shaped to focus on and endlessly discover the ways in which the world is falling apart. Armageddon was always just a few years away, and the systems built by man were hopelessly imperfect. At 25, I belong to a generation familiar with accelerating complexity. We are post Cold War children; 9–11 watchers; inheritors of globalism; formed by the Internet.

By my teenage years I had lost faith in my religion and searched for meaning in philosophy, poetry, and music. I identified with the archetypal Thinker and longed to live a simple life devoted to thinking, letting the world fall a part around me. Because, frankly, while I stopped believing in religion, I had not stopped believing we live in the “End of Days”.

I entered University as a Philosophy major, enthusiastic to pursue the mysterious and metaphysical. By my second week of college, my father had committed suicide. I don’t bring this up in hope of pity, but to give some context on my choices and background, which influence me to this day. The hopelessness I felt after my father’s death made studying Philosophy unbearable; I needed a means to find solutions, not another way to question my reality. While I was given the choice to take a break from school, I decided to spend the second semester of my freshman year taking art and music classes. In practicing art I reflected deeply on what I wanted my life to mean, letting my subconscious work on my grief while my hands reflected the creativity I desired in life.

One morning, during that semester, I woke up and had a strong intuition that I would find my purpose on that day. Within two hours of waking up, I had. While speaking with a friend, I noticed a dreamcatcher on the wall of her roommate. The dreamcatcher stirred inspiration in me so much, I took the idea of it seriously. In a moment, I decided that I would pursue engineering so that I might one day be a neural engineer. My hope was to catch dreams as they played through the mind, displaying them for the dreamer to see once they awoke.

Through the next 4 years of college I switched into biomedical engineering and took on research in a neuroscience lab. I became a Mellon Mays fellow and experimented with wearable consumer technology that interfaced with the brain. And I integrated my creative streak by taking on roles in design and engineering projects that utilized art and explored meaning.

My passion for dreaming has remained, but I decided to pursue a career in consulting after finishing up my bachelors, because I think there are lessons in professional work that are crucial to the practice of research and development. As an engineering intern at Two Bit Circus I developed a passion for building systems that educate and develop young minds through Play. As a software consultant at Accenture, I have developed a respect for the process of teamwork, especially across the borders of government and industry. This has cultivated in me an interest in the development of ‘Smart Cities,’ principally as they relate to the people that inhabit them.

Recently I have dived into working on virtual reality development, by taking a course with Udacity. Through Udacity I came to find out about the Georgia Tech Online Masters in Computer Science. The program immediately piqued my interest because I can complete it while working, it is affordable, and it is geared towards those who wish to learn Computer Science even if they don’t have a background in it. This makes evident that Georgia Tech values inclusion in tech and access to education, both of which matter deeply to me.

Marc Andreessen said in 2011, “Software is eating the world.” This is true at the nexus of my passions, so learning its practice is a logical next step. With this degree, I hope to build systems that capture dreams, educate kids, and empower citizens.

Thank you for the consideration.